import sys
import os
NOP = 0x90
offsets_and_values = {
0x00030170: 0x00,
0x000A94D0: NOP, 0x000A94D1: NOP, 0x000A94D2: NOP, 0x000A94D3: NOP, 0x000A94D4: NOP, 0x000A94D5: NOP, 0x000A94D6: NOP, 0x000A94D7: NOP, 0x000A94D8: NOP, 0x000A94D9: NOP, 0x000A94DA: NOP, 0x000A94DB: NOP, 0x000A94DC: NOP, 0x000A94DD: NOP, 0x000A94DE: NOP, 0x000A94DF: NOP, 0x000A94E0: NOP, 0x000A94E1: NOP, 0x000A94E2: NOP, 0x000A94E3: NOP, 0x000A94E4: NOP, 0x000A94E5: NOP, 0x000A94E6: NOP, 0x000A94E7: NOP, 0x000A94E8: NOP, 0x000A94E9: NOP, 0x000A94EA: NOP, 0x000A94EB: NOP, 0x000A94EC: NOP, 0x000A94ED: NOP, 0x000A94EE: NOP, 0x000A94EF: NOP, 0x000A94F0: NOP, 0x000A94F1: NOP, 0x000A94F2: NOP, 0x000A94F3: NOP, 0x000A94F4: NOP, 0x000A94F5: NOP, 0x000A94F6: NOP, 0x000A94F7: NOP, 0x000A94F8: NOP, 0x000A94F9: NOP, 0x000A94FA: NOP, 0x000A94FB: NOP, 0x000A94FC: NOP, 0x000A94FD: NOP, 0x000A94FE: NOP, 0x000A94FF: NOP, 0x000A9500: NOP, 0x000A9501: NOP, 0x000A9502: NOP, 0x000A9503: NOP, 0x000A9504: NOP, 0x000A9505: NOP, 0x000A9506: NODiscover gists
| #!/bin/bash | |
| # Install an SSH key on an instance automatically and then configure an | |
| # SSM proxy to the instance which will be used by SSH. | |
| # | |
| # Copy this script to ~/.ssh/ssm-proxy and make it executable: | |
| # | |
| # chmod +x ~/.ssh/ssm-proxy | |
| # | |
| # This script should be set as the ProxyCommand. For example: | |
| # |
| { | |
| "gravity": false | |
| } |
使用JAV金鸡儿奖官网附带的工具JAV SQL 查询器,可查询各种类别的JavDB TOP250影片:
及分年数据(存在部分重复影片,原始数据的问题):
A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.
| import Foundation | |
| import CoreImage | |
| struct CleanupError: LocalizedError { | |
| let message: String | |
| var errorDescription: String? { message } | |
| } | |
| /// Remove a rectangle using the OS inpainting model. Rect uses whole pixels from the top-left of the oriented image. | |
| public func cleanUp(_ image: CIImage, rect: CGRect) throws -> CIImage { |
| struct ContentView: View { | |
| var body: some View { | |
| let names = [ | |
| ["appstore.app.dashed", "buildings.3d", "emoji.chicken.face"], | |
| ["person.text.rectangle.and.nfc", "secure.element", "laugh.bubble.tapback.2.he"], | |
| ["apple.news", "apple.podcasts.square.stack", "apple.slice"], | |
| ] | |
| VStack(spacing: 20) { | |
| Grid(horizontalSpacing: 20, verticalSpacing: 20) { | |
| ForEach(names, id: \.self) { nameRow in |
Based on the excellent Solarized (Dark) created by Ethan Schoonover. For source code, check the main Solarized repository on GitHub.
Open and save Solarized Dark.terminal.
Import from the “Profiles” tab in the settings of Terminal.app or just double-click the file after downloading.
